@article{Shen-Orr2010,
abstract = {We describe cell type-specific significance analysis of microarrays (csSAM) for analyzing differential gene expression for each cell type in a biological sample from microarray data and relative cell-type frequencies. First, we validated csSAM with predesigned mixtures and then applied it to whole-blood gene expression datasets from stable post-transplant kidney transplant recipients and those experiencing acute transplant rejection, which revealed hundreds of differentially expressed genes that were otherwise undetectable. {\textcopyright} 2010 Nature America, Inc. All rights reserved.},
author = {Shen-Orr, Shai S. and Tibshirani, Robert and Khatri, Purvesh and Bodian, Dale L. and Staedtler, Frank and Perry, Nicholas M. and Hastie, Trevor and Sarwal, Minnie M. and Davis, Mark M. and Butte, Atul J.},
doi = {10.1038/nmeth.1439},
file = {:C$\backslash$:/Users/pfistsa4/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shen-Orr et al. - 2010 - Cell type-specific gene expression differences in complex tissues.pdf:pdf},
issn = {15487091},
journal = {Nature Methods},
keywords = {Gene expression,Microarray analysis},
month = {apr},
number = {4},
pages = {287--289},
pmid = {20208531},
publisher = {Nature Publishing Group},
title = {{Cell type-specific gene expression differences in complex tissues}},
url = {http://www.nature.com/naturemethods/.},
volume = {7},
year = {2010}
}
@article{Wang2016,
author = {Wang, Niya and Hoffman, Eric P and Chen, Lulu and Chen, Li and Zhang, Zhen and Liu, Chunyu and Yu, Guoqiang and Herrington, David M and Clarke, Robert and Wang, Yue},
journal = {Scientific Reports},
month = {jan},
pages = {18909},
publisher = {The Author(s)},
title = {{Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues}},
url = {https://doi.org/10.1038/srep18909 http://10.0.4.14/srep18909 https://www.nature.com/articles/srep18909{\#}supplementary-information},
volume = {6},
year = {2016}
}
@article{Gong2013,
abstract = {Summary: For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are confounded by relative proportions of cell types involved. In this note, we introduce an efficient pipeline: DeconRNASeq, an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It adopts a globally optimized non-negative decomposition algorithm through quadratic programming for estimating the mixing proportions of distinctive tissue types in next-generation sequencing data. We demonstrated the feasibility and validity of DeconRNASeq across a range of mixing levels and sources using mRNA-Seq data mixed in silico at known concentrations. We validated our computational approach for various benchmark data, with high correlation between our predicted cell proportions and the real fractions of tissues. Our study provides a rigorous, quantitative and high-resolution tool as a prerequisite to use mRNA-Seq data. The modularity of package design allows an easy deployment of custom analytical pipelines for data from other high-throughput platforms.Availability: DeconRNASeq is written in R, and is freely available at http://bioconductor.org/packages.Contact:tinggong@gmail.comSupplementary information:Supplementary data are available at Bioinformatics online.},
author = {Gong, Ting and Szustakowski, Joseph D},
doi = {10.1093/bioinformatics/btt090},
issn = {1367-4803},
journal = {Bioinformatics},
month = {feb},
number = {8},
pages = {1083--1085},
title = {{DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data}},
url = {https://doi.org/10.1093/bioinformatics/btt090},
volume = {29},
year = {2013}
}
@article{Hunt2018,
abstract = {Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA).We propose dtangle, a new cell type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell type proportions using publicly available, often cross-platform, reference data. We evaluate dtangle on 11 benchmark datasets showing that dtangle is competitive with published deconvolution methods, is robust to outliers and selection of tuning parameters, and is fast. As a case study, we investigate the human immune response to Lyme disease. dtangle's estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status.dtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io).Supplementary data are available at Bioinformatics online.},
author = {Hunt, Gregory J and Freytag, Saskia and Bahlo, Melanie and Gagnon-Bartsch, Johann A},
doi = {10.1093/bioinformatics/bty926},
issn = {1367-4803},
journal = {Bioinformatics},
month = {nov},
number = {12},
pages = {2093--2099},
title = {dtangle: accurate and robust cell type deconvolution},
url = {https://doi.org/10.1093/bioinformatics/bty926},
volume = {35},
year = {2018}
}
@article{Newman2015,
abstract = {{\textcopyright} 2015 Nature America, Inc. We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).},
author = {Newman, Aaron M. and Liu, Chih Long and Green, Michael R. and Gentles, Andrew J. and Feng, Weiguo and Xu, Yue and Hoang, Chuong D. and Diehn, Maximilian and Alizadeh, Ash A.},
doi = {10.1038/nmeth.3337},
file = {:C$\backslash$:/Users/admin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Newman et al. - 2015 - Robust enumeration of cell subsets from tissue expression profiles.pdf:pdf},
issn = {15487105},
journal = {Nature Methods},
number = {5},
pages = {453--457},
title = {{Robust enumeration of cell subsets from tissue expression profiles}},
volume = {12},
year = {2015}
}
@article{Newman2019,
abstract = {Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.},
author = {Newman, Aaron M. and Steen, Chlo{\'{e}} B. and Liu, Chih Long and Gentles, Andrew J. and Chaudhuri, Aadel A. and Scherer, Florian and Khodadoust, Michael S. and Esfahani, Mohammad S. and Luca, Bogdan A. and Steiner, David and Diehn, Maximilian and Alizadeh, Ash A.},
doi = {10.1038/s41587-019-0114-2},
file = {:C$\backslash$:/Users/pfistsa4/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Newman et al. - 2019 - Determining cell type abundance and expression from bulk tissues with digital cytometry.pdf:pdf},
issn = {15461696},
journal = {Nature Biotechnology},
keywords = {Cancer microenvironment,Computational biology and bioinformatics,Immunology},
month = {jul},
number = {7},
pages = {773--782},
pmid = {31061481},
publisher = {Nature Research},
title = {{Determining cell type abundance and expression from bulk tissues with digital cytometry}},
url = {https://doi.org/10.1038/s41587-019-0114-2},
volume = {37},
year = {2019}
}
@article{Abbas2009,
abstract = {Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with a complex spectrum of cellular and molecular characteristics including several dramatic changes in the populations of peripheral leukocytes. These changes include general leukopenia, activation of B and T cells, and maturation of granulocytes. The manifestation of SLE in peripheral blood is central to the disease but is incompletely understood. A technique for rigorously characterizing changes in mixed populations of cells, microarray expression deconvolution, has been applied to several areas of biology but not to SLE or to blood. Here we demonstrate that microarray expression deconvolution accurately quantifies the constituents of real blood samples and mixtures of immune-derived cell lines. We characterize a broad spectrum of peripheral leukocyte cell types and states in SLE to uncover novel patterns including: specific activation of NK and T helper lymphocytes, relationships of these patterns to each other, and correlations to clinical variables and measures. The expansion and activation of monocytes, NK cells, and T helper cells in SLE at least partly underlie this disease's prominent interferon signature. These and other patterns of leukocyte dynamics uncovered here correlate with disease severity and treatment, suggest potential new treatments, and extend our understanding of lupus pathology as a complex autoimmune disease involving many arms of the immune system.},
author = {Abbas, Alexander R. and Wolslegel, Kristen and Seshasayee, Dhaya and Modrusan, Zora and Clark, Hilary F.},
doi = {10.1371/journal.pone.0006098},
file = {:C$\backslash$:/Users/admin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Abbas et al. - 2009 - Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.pdf:pdf},
issn = {19326203},
journal = {PLoS ONE},
number = {7},
title = {{Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus}},
volume = {4},
year = {2009}
}
@article{Vallania2018,
abstract = {{\textcopyright} 2018, The Author(s). In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We find that immunoStates significantly reduces biological and technical biases. Importantly, we find that different methods have virtually no or minimal effect once the basis matrix is chosen. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.},
author = {Vallania, Francesco and Tam, Andrew and Lofgren, Shane and Schaffert, Steven and Azad, Tej D. and Bongen, Erika and Haynes, Winston and Alsup, Meia and Alonso, Michael and Davis, Mark and Engleman, Edgar and Khatri, Purvesh},
doi = {10.1038/s41467-018-07242-6},
file = {:C$\backslash$:/Users/admin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vallania et al. - 2018 - Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces bio.pdf:pdf},
issn = {20411723},
journal = {Nature Communications},
number = {1},
publisher = {Springer US},
title = {{Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases}},
url = {http://dx.doi.org/10.1038/s41467-018-07242-6},
volume = {9},
year = {2018}
}
@article{Monaco2019,
abstract = {The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. We characterized 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Our dataset was used, first, to identify sets of genes that are specific, are co-expressed, and have housekeeping roles across the 29 cell types. Then, we examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, we performed absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance. Absolute deconvolution is ready to use for PBMC transcriptomic data using our Shiny app (https://github.com/giannimonaco/ABIS). We benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. Our work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets.},
author = {Monaco, Gianni and Lee, Bernett and Xu, Weili and Mustafah, Seri and Hwang, You Yi and Carr{\'{e}}, Christophe and Burdin, Nicolas and Visan, Lucian and Ceccarelli, Michele and Poidinger, Michael and Zippelius, Alfred and {Pedro de Magalh{\~{a}}es}, Jo{\~{a}}o and Larbi, Anis},
doi = {10.1016/j.celrep.2019.01.041},
file = {:C$\backslash$:/Users/admin/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Monaco et al. - 2019 - RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.pdf:pdf},
issn = {22111247},
journal = {Cell Reports},
keywords = {RNA-seq,deconvolution,flow cytometry,gene modules,housekeeping,immune system,mRNA abundance,mRNA composition,mRNA heterogeneity,transcriptome},
number = {6},
pages = {1627--1640.e7},
title = {{RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types}},
volume = {26},
year = {2019}
}
@article{AvilaCobos2020,
abstract = {Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.},
author = {{Avila Cobos}, Francisco and Alquicira-Hernandez, Jos{\'{e}} and Powell, Joseph E. and Mestdagh, Pieter and {De Preter}, Katleen},
doi = {10.1038/s41467-020-19015-1},
file = {:C$\backslash$:/Users/pfistsa4/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Avila Cobos et al. - 2020 - Benchmarking of cell type deconvolution pipelines for transcriptomics data.pdf:pdf},
issn = {20411723},
journal = {Nature Communications},
keywords = {Computational biology and bioinformatics,Transcriptomics},
month = {dec},
number = {1},
pages = {1--14},
pmid = {33159064},
publisher = {Nature Research},
title = {{Benchmarking of cell type deconvolution pipelines for transcriptomics data}},
url = {https://doi.org/10.1038/s41467-020-19015-1},
volume = {11},
year = {2020}
}
